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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/"
)
```
# unpivotr
<!-- badges: start -->
[](https://travis-ci.org/nacnudus/unpivotr)
[](https://ci.appveyor.com/project/nacnudus/unpivotr)
[](https://CRAN.R-project.org/package=unpivotr)
[](https://www.r-pkg.org/pkg/unpivotr)
[](https://app.codecov.io/gh/nacnudus/unpivotr)
[](https://github.com/nacnudus/unpivotr/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
[unpivotr](https://github.com/nacnudus/unpivotr) deals with non-tabular data,
especially from spreadsheets. Use unpivotr when your source data has any of
these 'features':
* Multi-headered hydra
* Meaningful formatting
* Headers anywhere but at the top of each column
* Non-text headers e.g. dates
* Other stuff around the table
* Several similar tables in one sheet
* Sentinel values
* Superscript symbols
* Meaningful comments
* Nested HTML tables
If that list makes your blood boil, you'll enjoy the function names.
* `behead()` deals with multi-headered hydra tables one layer of headers at a
time, working from the edge of the table inwards. It's a bit like using
`header = TRUE` in `read.csv()`, but because it's a function, you can apply it
to as many layers of headers as you need. You end up with all the headers in
columns.
* `spatter()` is like `tidyr::spread()` but preserves mixed data types. You get
into a mixed-data-type situation by delaying type coercion until *after* the
table is tidy (rather than before, like `read.csv()` et al). And yes, it
usually follows `behead()`.
More positive, corrective functions:
* `justify()` aligns column headers before `behead()`ing, and has deliberate
moral overtones.
* `enhead()` attaches a header to the body of the data, *a la* Frankenstein.
The effect is the same as `behead()`, but is more powerful because you can
choose exactly which header cells you want, paying attention to formatting
(which `behead()` doesn't understand).
* `isolate_sentinels()` separates meaningful symbols like `"N/A"` or
`"confidential"` from the rest of the data, giving them some time alone think
about what they've done.
* `partition()` takes a sheet with several tables on it, and slashes into pieces
that each contain one table. You can then unpivot each table in turn with
`purrr::map()` or similar.
## Make cells tidy
Unpivotr uses data where each cells is represented by one row in a dataframe.
Like this.
What can you do with tidy cells? The best places to start are:
* [Spreadsheet Munging
Strategies](https://nacnudus.github.io/spreadsheet-munging-strategies/), a
free, online cookbook using [tidyxl](https://github.com/nacnudus/tidyxl/) and
[unpivotr](https://github.com/nacnudus/unpivotr)
* [Screencasts](https://www.youtube.com/watch?v=1sinC7wsS5U) on YouTube.
* [Worked examples](https://github.com/nacnudus/ukfarm) on GitHub.
Otherwise the basic idea is:
1. Read the data with a specialist tool.
* For spreadsheets, use [tidyxl](https://nacnudus.github.io/tidyxl/).
* For plain text files, you might soon be able to use
[readr](https://readr.tidyverse.org), but for now you'll have to install a
pull-request on that package with
`devtools::install_github("tidyverse/readr#760")`.
* For tables in html pages, use `unpivotr::tidy_html()`
* For data frames, use `unpivotr::as_cells()` -- this should be a last
resort, because by the time the data is in a conventional data frame, it
is often too late -- formatting has been lost, and most data types have
been coerced to strings.
1. Either `behead()` straight away, else `dplyr::filter()` separately for the
header cells and the data cells, and then recombine with `enhead()`.
1. `spatter()` so that each column has one data type.
```{r}
library(unpivotr)
library(tidyverse)
x <- purpose$`up-left left-up`
x # A pivot table in a conventional data frame. Four levels of headers, in two
# rows and two columns.
y <- as_cells(x) # 'Tokenize' or 'melt' the data frame into one row per cell
y
rectify(y) # useful for reviewing the melted form as though in a spreadsheet
y %>%
behead("up-left", "sex") %>% # Strip headers
behead("up", "life-satisfication") %>% # one
behead("left-up", "qualification") %>% # by
behead("left", "age-band") %>% # one.
select(-row, -col, -data_type, count = chr) %>% # cleanup
mutate(count = as.integer(count))
```
Note the compass directions in the code above, which hint to `behead()` where to
find the header cell for each data cell.
* `"up-left"` means the header (`Female`, `Male`) is positioned up and to the
left of the columns of data cells it describes.
* `"up"` means the header (`0 - 6`, `7 - 10`) is positioned directly above the
columns of data cells it describes.
* `"left-up"` means the header (`Bachelor's degree`, `Certificate`, etc.) is
positioned to the left and upwards of the rows of data cells it describes.
* `"left"` means the header (`15 - 24`, `25 - 44`, etc.) is positioned directly to
the left of the rows of data cells it describes.
## Installation
```{r, echo = TRUE, eval = FALSE}
# install.packages("devtools") # If you don't already have devtools
devtools::install_github("nacnudus/unpivotr", build_vignettes = TRUE)
```
The version 0.4.0 release had somee breaking changes. See `NEWS.md` for
details. The previous version can be installed as follow:
```r
devtools::install_version("unpivotr", version = "0.3.1", repos = "http://cran.us.r-project.org")
```
## Similar projects
[unpivotr](https://github.com/nacnudus/unpivotr) is inspired by
[Databaker](https://github.com/sensiblecodeio/databaker), a collaboration
between the [United Kingdom Office of National Statistics](https://www.ons.gov.uk/)
and [The Sensible Code Company](https://sensiblecode.io/).
[unpivotr](https://github.com/nacnudus/unpivotr).
[jailbreaker](https://github.com/rsheets/jailbreakr) attempts to extract
non-tabular data from spreadsheets into tabular structures automatically via
some clever algorithms. [unpivotr](https://github.com/nacnudus/unpivotr)
differs by being less magic, and equipping you to express what you want to do.